Python卷积神经网络图片分类框架详解分析
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2022-03-22 12:33:47
【人工智能项目】卷积神经网络图片分类框架本次硬核分享当时做图片分类的工作,主要是整理了一个图片分类的框架,如果想换模型,引入新模型,在config中修改即可。那么走起来瓷!!!整体结构config在c...
【人工智能项目】卷积神经网络图片分类框架
本次硬核分享当时做图片分类的工作,主要是整理了一个图片分类的框架,如果想换模型,引入新模型,在config中修改即可。那么走起来瓷!!!
整体结构
config
在config文件夹下的config.py中主要定义数据集的位置,训练轮数,batch_size以及本次选用的模型。
# 定义训练集和测试集的路径 train_data_path = "./data/train/" train_anno_path = "./data/train.csv" test_data_path = "./data/test/" # 定义多线程 num_workers = 8 # 定义batch_size大小 batch_size = 8 # 定义训练轮数 epochs = 20 # 定义k折交叉验证 k = 5 # 定义模型选择 # inception_v3_google inceptionv4 # vgg16 # resnet50 resnet101 resnet152 resnext50_32x4d resnext101_32x8d wide_resnet50_2 wide_resnet101_2 # senet154 se_resnet50 se_resnet101 se_resnet152 se_resnext50_32x4d se_resnext101_32x4d # nasnetalarge pnasnet5large # densenet121 densenet161 densenet169 densenet201 # efficientnet-b0 efficientnet-b1 efficientnet-b2 efficientnet-b3 efficientnet-b4 efficientnet-b5 efficientnet-b6 efficientnet-b7 # xception # squeezenet1_0 squeezenet1_1 # mobilenet_v2 # mnasnet0_5 mnasnet0_75 mnasnet1_0 mnasnet1_3 # shufflenet_v2_x0_5 shufflenet_v2_x1_0 model_name = "vgg16" # 定义分类类别 num_classes = 102 # 定义图片尺寸 img_width = 320 img_height = 320
data
data文件夹存放了train和test图片信息。
在train.csv中的存放图片名称以及对应的标签
dataloader
dataloader里面主要有data.py和data_augmentation.py文件。其中一个用于读取数据,另外一个用于数据增强操作。
import torch from pil import image from torch.utils.data.dataset import dataset import numpy as np import pil from torchvision import transforms from config import config import os import cv2 # 定义dataset和transform # 将df转换成标准的numpy array形式 def get_anno(path, images_path): data = [] with open(path) as f: for line in f: idx, label = line.strip().split(',') data.append((os.path.join(images_path, idx), int(label))) return np.array(data) # 定义读取traindata,读取df文件 # 通过df的idx,来获取image_path和label class traindataset(dataset): def __init__(self, data, transform=none): self.data = data self.transform = transform def __getitem__(self, idx): img_path, label = self.data[idx] img = image.open(img_path).convert('rgb') #img = cv2.imread(img_path) #img = cv2.cvtcolor(img, cv2.color_bgr2rgb) if self.transform is not none: img = self.transform(img) return img, int(label) def __len__(self): return len(self.data) # 通过文件路径来读取测试图片 class testdataset(dataset): def __init__(self, img_path, transform=none): self.img_path = img_path if transform is not none: self.transform = transform else: self.transform = none def __getitem__(self, index): img = image.open(self.img_path[index]).convert('rgb') # img = cv2.imread(self.img_path[index]) # img = cv2.cvtcolor(img, cv2.color_bgr2rgb) if self.transform is not none: img = self.transform(img) return img def __len__(self): return len(self.img_path) # train_transform = transforms.compose([ # transforms.resize([config.img_width, config.img_height]), # transforms.randomrotation(10), # transforms.colorjitter(brightness=0.3, contrast=0.2), # transforms.randomhorizontalflip(), # transforms.totensor(), # range [0, 255] -> [0.0,1.0] # transforms.normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # ]) train_transform = transforms.compose([ transforms.pad(4, padding_mode='reflect'), transforms.randomrotation(10), transforms.randomresizedcrop([config.img_width, config.img_height]), transforms.randomhorizontalflip(), transforms.totensor(), transforms.normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) val_transform = transforms.compose([ transforms.randomresizedcrop([config.img_width, config.img_height]), transforms.totensor(), transforms.normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) test_transform = transforms.compose([ transforms.randomresizedcrop([config.img_width, config.img_height]), transforms.totensor(), transforms.normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ])
import random from __future__ import division import cv2 import numpy as np from numpy import random import math from sklearn.utils import shuffle # 固定角度随机旋转 class fixedrotation(object): def __init__(self, angles): self.angles = angles def __call__(self, img): return fixed_rotate(img, self.angles) def fixed_rotate(img, angles): angles = list(angles) angles_num = len(angles) index = random.randint(0, angles_num - 1) return img.rotate(angles[index]) __all__ = ['compose','randomhflip', 'randomuppercrop', 'resize', 'uppercrop', 'randombottomcrop',"randomerasing", 'bottomcrop', 'normalize', 'randomswapchannels', 'randomrotate', 'randomhshift',"centercrop","randomvflip", 'expandborder', 'randomresizedcrop','randomdowncrop', 'downcrop', 'resizedcrop',"fixrandomrotate"] def rotate_nobound(image, angle, center=none, scale=1.): (h, w) = image.shape[:2] # if the center is none, initialize it as the center of # the image if center is none: center = (w // 2, h // 2) # perform the rotation m = cv2.getrotationmatrix2d(center, angle, scale) rotated = cv2.warpaffine(image, m, (w, h)) return rotated def scale_down(src_size, size): w, h = size sw, sh = src_size if sh < h: w, h = float(w * sh) / h, sh if sw < w: w, h = sw, float(h * sw) / w return int(w), int(h) def fixed_crop(src, x0, y0, w, h, size=none): out = src[y0:y0 + h, x0:x0 + w] if size is not none and (w, h) != size: out = cv2.resize(out, (size[0], size[1]), interpolation=cv2.inter_cubic) return out class fixrandomrotate(object): def __init__(self, angles=[0,90,180,270], bound=false): self.angles = angles self.bound = bound def __call__(self,img): do_rotate = random.randint(0, 4) angle=self.angles[do_rotate] if self.bound: img = rotate_bound(img, angle) else: img = rotate_nobound(img, angle) return img def center_crop(src, size): h, w = src.shape[0:2] new_w, new_h = scale_down((w, h), size) x0 = int((w - new_w) / 2) y0 = int((h - new_h) / 2) out = fixed_crop(src, x0, y0, new_w, new_h, size) return out def bottom_crop(src, size): h, w = src.shape[0:2] new_w, new_h = scale_down((w, h), size) x0 = int((w - new_w) / 2) y0 = int((h - new_h) * 0.75) out = fixed_crop(src, x0, y0, new_w, new_h, size) return out def rotate_bound(image, angle): # grab the dimensions of the image and then determine the # center h, w = image.shape[:2] (cx, cy) = (w // 2, h // 2) m = cv2.getrotationmatrix2d((cx, cy), angle, 1.0) cos = np.abs(m[0, 0]) sin = np.abs(m[0, 1]) # compute the new bounding dimensions of the image nw = int((h * sin) + (w * cos)) nh = int((h * cos) + (w * sin)) # adjust the rotation matrix to take into account translation m[0, 2] += (nw / 2) - cx m[1, 2] += (nh / 2) - cy rotated = cv2.warpaffine(image, m, (nw, nh)) return rotated class compose(object): def __init__(self, transforms): self.transforms = transforms def __call__(self, img): for t in self.transforms: img = t(img) return img class randomrotate(object): def __init__(self, angles, bound=false): self.angles = angles self.bound = bound def __call__(self,img): do_rotate = random.randint(0, 2) if do_rotate: angle = np.random.uniform(self.angles[0], self.angles[1]) if self.bound: img = rotate_bound(img, angle) else: img = rotate_nobound(img, angle) return img class randombrightness(object): def __init__(self, delta=10): assert delta >= 0 assert delta <= 255 self.delta = delta def __call__(self, image): if random.randint(2): delta = random.uniform(-self.delta, self.delta) image = (image + delta).clip(0.0, 255.0) # print('randombrightness,delta ',delta) return image class randomcontrast(object): def __init__(self, lower=0.9, upper=1.05): self.lower = lower self.upper = upper assert self.upper >= self.lower, "contrast upper must be >= lower." assert self.lower >= 0, "contrast lower must be non-negative." # expects float image def __call__(self, image): if random.randint(2): alpha = random.uniform(self.lower, self.upper) # print('contrast:', alpha) image = (image * alpha).clip(0.0,255.0) return image class randomsaturation(object): def __init__(self, lower=0.8, upper=1.2): self.lower = lower self.upper = upper assert self.upper >= self.lower, "contrast upper must be >= lower." assert self.lower >= 0, "contrast lower must be non-negative." def __call__(self, image): if random.randint(2): alpha = random.uniform(self.lower, self.upper) image[:, :, 1] *= alpha # print('randomsaturation,alpha',alpha) return image class randomhue(object): def __init__(self, delta=18.0): assert delta >= 0.0 and delta <= 360.0 self.delta = delta def __call__(self, image): if random.randint(2): alpha = random.uniform(-self.delta, self.delta) image[:, :, 0] += alpha image[:, :, 0][image[:, :, 0] > 360.0] -= 360.0 image[:, :, 0][image[:, :, 0] < 0.0] += 360.0 # print('randomhue,alpha:', alpha) return image class convertcolor(object): def __init__(self, current='bgr', transform='hsv'): self.transform = transform self.current = current def __call__(self, image): if self.current == 'bgr' and self.transform == 'hsv': image = cv2.cvtcolor(image, cv2.color_bgr2hsv) elif self.current == 'hsv' and self.transform == 'bgr': image = cv2.cvtcolor(image, cv2.color_hsv2bgr) else: raise notimplementederror return image class randomswapchannels(object): def __call__(self, img): if np.random.randint(2): order = np.random.permutation(3) return img[:,:,order] return img class randomcrop(object): def __init__(self, size): self.size = size def __call__(self, image): h, w, _ = image.shape new_w, new_h = scale_down((w, h), self.size) if w == new_w: x0 = 0 else: x0 = random.randint(0, w - new_w) if h == new_h: y0 = 0 else: y0 = random.randint(0, h - new_h) out = fixed_crop(image, x0, y0, new_w, new_h, self.size) return out class randomresizedcrop(object): def __init__(self, size,scale=(0.49, 1.0), ratio=(1., 1.)): self.size = size self.scale = scale self.ratio = ratio def __call__(self,img): if random.random() < 0.2: return cv2.resize(img,self.size) h, w, _ = img.shape area = h * w d=1 for attempt in range(10): target_area = random.uniform(self.scale[0], self.scale[1]) * area aspect_ratio = random.uniform(self.ratio[0], self.ratio[1]) new_w = int(round(math.sqrt(target_area * aspect_ratio))) new_h = int(round(math.sqrt(target_area / aspect_ratio))) if random.random() < 0.5: new_h, new_w = new_w, new_h if new_w < w and new_h < h: x0 = random.randint(0, w - new_w) y0 = (random.randint(0, h - new_h))//d out = fixed_crop(img, x0, y0, new_w, new_h, self.size) return out # fallback return center_crop(img, self.size) class downcrop(): def __init__(self, size, select, scale=(0.36,0.81)): self.size = size self.scale = scale self.select = select def __call__(self,img, attr_idx): if attr_idx not in self.select: return img, attr_idx if attr_idx == 0: self.scale=(0.64,1.0) h, w, _ = img.shape area = h * w s = (self.scale[0]+self.scale[1])/2.0 target_area = s * area new_w = int(round(math.sqrt(target_area))) new_h = int(round(math.sqrt(target_area))) if new_w < w and new_h < h: dw = w-new_w x0 = int(0.5*dw) y0 = h-new_h out = fixed_crop(img, x0, y0, new_w, new_h, self.size) return out, attr_idx # fallback return center_crop(img, self.size), attr_idx class resizedcrop(object): def __init__(self, size, select,scale=(0.64, 1.0), ratio=(3. / 4., 4. / 3.)): self.size = size self.scale = scale self.ratio = ratio self.select = select def __call__(self,img, attr_idx): if attr_idx not in self.select: return img, attr_idx h, w, _ = img.shape area = h * w d=1 if attr_idx == 2: self.scale=(0.36,0.81) d=2 if attr_idx == 0: self.scale=(0.81,1.0) target_area = (self.scale[0]+self.scale[1])/2.0 * area # aspect_ratio = random.uniform(self.ratio[0], self.ratio[1]) new_w = int(round(math.sqrt(target_area))) new_h = int(round(math.sqrt(target_area))) # if random.random() < 0.5: # new_h, new_w = new_w, new_h if new_w < w and new_h < h: x0 = (w - new_w)//2 y0 = (h - new_h)//d//2 out = fixed_crop(img, x0, y0, new_w, new_h, self.size) # cv2.imshow('{}_img'.format(idx2attr_map[attr_idx]), img) # cv2.imshow('{}_crop'.format(idx2attr_map[attr_idx]), out) # # cv2.waitkey(0) return out, attr_idx # fallback return center_crop(img, self.size), attr_idx class randomhflip(object): def __call__(self, image): if random.randint(2): return cv2.flip(image, 1) else: return image class randomvflip(object): def __call__(self, image): if random.randint(2): return cv2.flip(image, 0) else: return image class hflip(object): def __init__(self,dohflip): self.dohflip = dohflip def __call__(self, image): if self.dohflip: return cv2.flip(image, 1) else: return image class centercrop(object): def __init__(self, size): self.size = size def __call__(self, image): return center_crop(image, self.size) class uppercrop(): def __init__(self, size, scale=(0.09, 0.64)): self.size = size self.scale = scale def __call__(self,img): h, w, _ = img.shape area = h * w s = (self.scale[0]+self.scale[1])/2.0 target_area = s * area new_w = int(round(math.sqrt(target_area))) new_h = int(round(math.sqrt(target_area))) if new_w < w and new_h < h: dw = w-new_w x0 = int(0.5*dw) y0 = 0 out = fixed_crop(img, x0, y0, new_w, new_h, self.size) return out # fallback return center_crop(img, self.size) class randomuppercrop(object): def __init__(self, size, select, scale=(0.09, 0.64), ratio=(3. / 4., 4. / 3.)): self.size = size self.scale = scale self.ratio = ratio self.select = select def __call__(self,img, attr_idx): if random.random() < 0.2: return img, attr_idx if attr_idx not in self.select: return img, attr_idx h, w, _ = img.shape area = h * w for attempt in range(10): s = random.uniform(self.scale[0], self.scale[1]) d = 0.1 + (0.3 - 0.1) / (self.scale[1] - self.scale[0]) * (s - self.scale[0]) target_area = s * area aspect_ratio = random.uniform(self.ratio[0], self.ratio[1]) new_w = int(round(math.sqrt(target_area * aspect_ratio))) new_h = int(round(math.sqrt(target_area / aspect_ratio))) # new_w = int(round(math.sqrt(target_area))) # new_h = int(round(math.sqrt(target_area))) if new_w < w and new_h < h: dw = w-new_w x0 = random.randint(int((0.5-d)*dw), int((0.5+d)*dw)+1) y0 = (random.randint(0, h - new_h))//10 out = fixed_crop(img, x0, y0, new_w, new_h, self.size) return out, attr_idx # fallback return center_crop(img, self.size), attr_idx class randomdowncrop(object): def __init__(self, size, select, scale=(0.36, 0.81), ratio=(3. / 4., 4. / 3.)): self.size = size self.scale = scale self.ratio = ratio self.select = select def __call__(self,img, attr_idx): if random.random() < 0.2: return img, attr_idx if attr_idx not in self.select: return img, attr_idx if attr_idx == 0: self.scale=(0.64,1.0) h, w, _ = img.shape area = h * w for attempt in range(10): s = random.uniform(self.scale[0], self.scale[1]) d = 0.1 + (0.3 - 0.1) / (self.scale[1] - self.scale[0]) * (s - self.scale[0]) target_area = s * area aspect_ratio = random.uniform(self.ratio[0], self.ratio[1]) new_w = int(round(math.sqrt(target_area * aspect_ratio))) new_h = int(round(math.sqrt(target_area / aspect_ratio))) # # new_w = int(round(math.sqrt(target_area))) # new_h = int(round(math.sqrt(target_area))) if new_w < w and new_h < h: dw = w-new_w x0 = random.randint(int((0.5-d)*dw), int((0.5+d)*dw)+1) y0 = (random.randint((h - new_h)*9//10, h - new_h)) out = fixed_crop(img, x0, y0, new_w, new_h, self.size) # cv2.imshow('{}_img'.format(idx2attr_map[attr_idx]), img) # cv2.imshow('{}_crop'.format(idx2attr_map[attr_idx]), out) # # cv2.waitkey(0) return out, attr_idx # fallback return center_crop(img, self.size), attr_idx class randomhshift(object): def __init__(self, select, scale=(0.0, 0.2)): self.scale = scale self.select = select def __call__(self,img, attr_idx): if attr_idx not in self.select: return img, attr_idx do_shift_crop = random.randint(0, 2) if do_shift_crop: h, w, _ = img.shape min_shift = int(w*self.scale[0]) max_shift = int(w*self.scale[1]) shift_idx = random.randint(min_shift, max_shift) direction = random.randint(0,2) if direction: right_part = img[:, -shift_idx:, :] left_part = img[:, :-shift_idx, :] else: left_part = img[:, :shift_idx, :] right_part = img[:, shift_idx:, :] img = np.concatenate((right_part, left_part), axis=1) # fallback return img, attr_idx class randombottomcrop(object): def __init__(self, size, select, scale=(0.4, 0.8)): self.size = size self.scale = scale self.select = select def __call__(self,img, attr_idx): if attr_idx not in self.select: return img, attr_idx h, w, _ = img.shape area = h * w for attempt in range(10): s = random.uniform(self.scale[0], self.scale[1]) d = 0.25 + (0.45 - 0.25) / (self.scale[1] - self.scale[0]) * (s - self.scale[0]) target_area = s * area new_w = int(round(math.sqrt(target_area))) new_h = int(round(math.sqrt(target_area))) if new_w < w and new_h < h: dw = w-new_w dh = h - new_h x0 = random.randint(int((0.5-d)*dw), min(int((0.5+d)*dw)+1,dw)) y0 = (random.randint(max(0,int(0.8*dh)-1), dh)) out = fixed_crop(img, x0, y0, new_w, new_h, self.size) return out, attr_idx # fallback return bottom_crop(img, self.size), attr_idx class bottomcrop(): def __init__(self, size, select, scale=(0.4, 0.8)): self.size = size self.scale = scale self.select = select def __call__(self,img, attr_idx): if attr_idx not in self.select: return img, attr_idx h, w, _ = img.shape area = h * w s = (self.scale[0]+self.scale[1])/3.*2. target_area = s * area new_w = int(round(math.sqrt(target_area))) new_h = int(round(math.sqrt(target_area))) if new_w < w and new_h < h: dw = w-new_w dh = h-new_h x0 = int(0.5*dw) y0 = int(0.9*dh) out = fixed_crop(img, x0, y0, new_w, new_h, self.size) return out, attr_idx # fallback return bottom_crop(img, self.size), attr_idx class resize(object): def __init__(self, size, inter=cv2.inter_cubic): self.size = size self.inter = inter def __call__(self, image): return cv2.resize(image, (self.size[0], self.size[0]), interpolation=self.inter) class expandborder(object): def __init__(self, mode='constant', value=255, size=(336,336), resize=false): self.mode = mode self.value = value self.resize = resize self.size = size def __call__(self, image): h, w, _ = image.shape if h > w: pad1 = (h-w)//2 pad2 = h - w - pad1 if self.mode == 'constant': image = np.pad(image, ((0, 0), (pad1, pad2), (0, 0)), self.mode, constant_values=self.value) else: image = np.pad(image,((0,0), (pad1, pad2),(0,0)), self.mode) elif h < w: pad1 = (w-h)//2 pad2 = w-h - pad1 if self.mode == 'constant': image = np.pad(image, ((pad1, pad2),(0, 0), (0, 0)), self.mode,constant_values=self.value) else: image = np.pad(image, ((pad1, pad2), (0, 0), (0, 0)),self.mode) if self.resize: image = cv2.resize(image, (self.size[0], self.size[0]),interpolation=cv2.inter_linear) return image class astypetoint(): def __call__(self, image, attr_idx): return image.clip(0,255.0).astype(np.uint8), attr_idx class astypetofloat(): def __call__(self, image, attr_idx): return image.astype(np.float32), attr_idx import matplotlib.pyplot as plt class normalize(object): def __init__(self,mean, std): ''' :param mean: rgb order :param std: rgb order ''' self.mean = np.array(mean).reshape(3,1,1) self.std = np.array(std).reshape(3,1,1) def __call__(self, image): ''' :param image: (h,w,3) rgb :return: ''' # plt.figure(1) # plt.imshow(image) # plt.show() return (image.transpose((2, 0, 1)) / 255. - self.mean) / self.std class randomerasing(object): def __init__(self, select,epsilon=0.5,sl=0.02, sh=0.09, r1=0.3, mean=[0.485, 0.456, 0.406]): self.epsilon = epsilon self.mean = mean self.sl = sl self.sh = sh self.r1 = r1 self.select = select def __call__(self, img,attr_idx): if attr_idx not in self.select: return img,attr_idx if random.uniform(0, 1) > self.epsilon: return img,attr_idx for attempt in range(100): area = img.shape[1] * img.shape[2] target_area = random.uniform(self.sl, self.sh) * area aspect_ratio = random.uniform(self.r1, 1 / self.r1) h = int(round(math.sqrt(target_area * aspect_ratio))) w = int(round(math.sqrt(target_area / aspect_ratio))) if w <= img.shape[2] and h <= img.shape[1]: x1 = random.randint(0, img.shape[1] - h) y1 = random.randint(0, img.shape[2] - w) if img.shape[0] == 3: # img[0, x1:x1+h, y1:y1+w] = random.uniform(0, 1) # img[1, x1:x1+h, y1:y1+w] = random.uniform(0, 1) # img[2, x1:x1+h, y1:y1+w] = random.uniform(0, 1) img[0, x1:x1 + h, y1:y1 + w] = self.mean[0] img[1, x1:x1 + h, y1:y1 + w] = self.mean[1] img[2, x1:x1 + h, y1:y1 + w] = self.mean[2] # img[:, x1:x1+h, y1:y1+w] = torch.from_numpy(np.random.rand(3, h, w)) else: img[0, x1:x1 + h, y1:y1 + w] = self.mean[1] # img[0, x1:x1+h, y1:y1+w] = torch.from_numpy(np.random.rand(1, h, w)) return img,attr_idx return img,attr_idx # if __name__ == '__main__': # import matplotlib.pyplot as plt # # # class fsaug(object): # def __init__(self): # self.augment = compose([ # astypetofloat(), # # randomhshift(scale=(0.,0.2),select=range(8)), # # randomrotate(angles=(-20., 20.), bound=true), # expandborder(select=range(8), mode='symmetric'),# symmetric # # resize(size=(336, 336), select=[ 2, 7]), # astypetoint() # ]) # # def __call__(self, spct,attr_idx): # return self.augment(spct,attr_idx) # # # trans = fsaug() # # img_path = '/media/gserver/data/fashionai/round2/train/images/coat_length_labels/0b6b4a2146fc8616a19fcf2026d61d50.jpg' # img = cv2.cvtcolor(cv2.imread(img_path),cv2.color_bgr2rgb) # img_trans,_ = trans(img,5) # # img_trans2,_ = trans(img,6) # print img_trans.max(), img_trans.min() # print img_trans.dtype # # plt.figure() # plt.subplot(221) # plt.imshow(img) # # plt.subplot(222) # plt.imshow(img_trans) # # # plt.subplot(223) # # plt.imshow(img_trans2) # # plt.imshow(img_trans2) # plt.show()
factory
factory里面主要定义了一些学习率,损失函数,优化器等之类的。
models
models中主要定义了常见的分类模型。
train.py
import os from sklearn.model_selection import kfold from torchvision import transforms import torch.utils.data from dataloader.data import traindataset,train_transform,val_transform,get_anno from factory.loss import * from models.model import model from config import config import numpy as np from utils import utils from factory.labelsmoothing import lsr def train(model_type, prefix): # df -> numpy.array()形式 data = get_anno(config.train_anno_path, config.train_data_path) # 5折交叉验证 skf = kfold(n_splits=config.k, random_state=233, shuffle=true) for flod_idx, (train_indices, val_indices) in enumerate(skf.split(data)): train_loader = torch.utils.data.dataloader( traindataset(data[train_indices], train_transform), batch_size=config.batch_size, shuffle=true, num_workers=config.num_workers, pin_memory=true ) val_loader = torch.utils.data.dataloader( traindataset(data[val_indices], val_transform), batch_size=config.batch_size, shuffle=false, num_workers=config.num_workers, pin_memory=true ) #criterion = focalloss(0.5) criterion = lsr() device = 'cuda' if torch.cuda.is_available() else 'cpu' model = model(model_type, config.num_classes, criterion, device=device, prefix=prefix, suffix=str(flod_idx)) for epoch in range(config.epochs): print('epoch: ', epoch) model.fit(train_loader) model.validate(val_loader) if __name__ == '__main__': model_type_list = [config.model_name] for model_type in model_type_list: train(model_type, "resize")
小结
本次主要给出一个图片分类的框架,方便快速的切换模型。
那下回见!!!欢迎大家多多点赞评论呀!!!
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